Census data in R with tidycensus

get_decennial requires the geography (level of aggregation) and variables parameters.

get_acs - American Community Survey data - based on samples, or estimates of the population. Dataframes returned from this function will have estimates and moe (margin of error for 90% CI).

# Obtain and view state populations from the 2010 US Census
state_pop <- get_decennial(geography = "state", variables = "P001001")

Getting data from the 2010 decennial Census

state_pop

# Obtain and view state median household income from the 2012-2016 American Community Survey
state_income <- get_acs(geography = "state", variables = "B19013_001")

Getting data from the 2012-2016 5-year ACS

state_income

Geography regions - legal entities (e.g. country) - statistical etities (e.g. tract)

tidycensus docs include geography available tidycensus "returns data in tidy foramt, but can return data in wide format with the output = "wide" parameter.

# Get an ACS dataset for Census tracts in Texas by setting the state
# this is the median household income
tx_income <- get_acs(geography = "tract",
                     variables = c(hhincome = "B19013_001"),
                     state = "TX")

Getting data from the 2012-2016 5-year ACS
Using FIPS code '48' for state 'TX'

# Inspect the dataset
head(tx_income)

# Get an ACS dataset for Census tracts in Travis County, TX
travis_income <- get_acs(geography = "tract",
                         variables = "B19013_001", 
                         state = "WA",
                         county = "King")

Getting data from the 2012-2016 5-year ACS
Using FIPS code '53' for state 'WA'
Using FIPS code '033' for 'King County'

# Inspect the dataset
head(travis_income)

# Return county data in wide format
or_wide <- get_acs(geography = "county", 
                     state = "OR",
                     variables = c(hhincome = "B19013_001", 
                            medage = "B01002_001"), 
                     output = "wide")

Getting data from the 2012-2016 5-year ACS
Using FIPS code '41' for state 'OR'

# Compare output to the tidy format from previous exercises
head(or_wide)

# Create a scatterplot
plot(or_wide$hhincomeE, or_wide$medageE)

To find census variables, look at online resources like Census Reporter - https://censusreporter.org/A

load_variables helps to find variables. takes year, dataset (acs5) and cache boolean, this can then be explored. Returns name, label, and concept fields.

ACS Variable structure B19001_002e B is a base (most data), (C collapsed, DP data profile, or S subject) 19001 is the table ID 002 the variable code within the table E is the estimate (optional in tidycensus, returns both the Estimate and Margin of error)

# Load variables from the 2012-2016 ACS
v16 <- load_variables(year = 2016,
           dataset = "acs5",
           cache = TRUE)
# Get variables from the ACS Data Profile
v16p <- load_variables(year = 2016,
                       dataset = "acs5/profile",
                       cache = TRUE)
# Set year and dataset to get variables from the 2000 Census SF3
v00 <- load_variables(year = 2000,
                      dataset = "sf3",
                      cache = TRUE)

# Filter for table B19001
filter(v16, str_detect(name, "B19001"))

# Use public transportation to search for related variables
filter(v16p, str_detect(label, fixed("public transportation", 
                                ignore_case = TRUE)))

get_acs(geography = "county", state = "WA", year = 2016, 
        variables = "DP03_0021P", survey = "acs1") %>% 
  mutate(NAME = gsub(" County, Washington", "", NAME)) %>% 
  ggplot(aes(estimate, reorder(NAME, estimate))) + 
  geom_point(size = 3, color = "navy") + 
  geom_errorbarh(aes(xmax = estimate + moe, xmin = estimate - moe)) + 
  scale_x_continuous(labels = scales::percent_format(scale = 1, accuracy = 1)) +
  hrbrthemes::theme_ipsum() + 
  labs(title = "Percent of workers commuting via public transit", 
       caption = "2016 ACS",
       x = "Percent",
       y = "County")

Getting data from the 2016 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using the ACS Data Profile
Using FIPS code '53' for state 'WA'

Tables and summary variables in tidycensus

get_acs and get_decennial have an optional table param that allows all the variables to be fetched at once without specifying a variable param.

# Download table "B19001"
wa_income <- get_acs(geography = "county", 
                 state = "WA", 
                 table = "B19001")

Getting data from the 2012-2016 5-year ACS
Using FIPS code '53' for state 'WA'

# Check out the first few rows of wa_income
wa_income

# Assign Census variables vector to race_vars  
race_vars <- c(White = "B03002_003", Black = "B03002_004", Native = "B03002_005", 
               Asian = "B03002_006", HIPI = "B03002_007", Hispanic = "B03002_012")

# Request a summary variable from the ACS
ca_race <- get_acs(geography = "county", 
                   state = "WA",
                   variables = race_vars, 
                   summary_var = "B03002_001")

Getting data from the 2012-2016 5-year ACS
Using FIPS code '53' for state 'WA'

# Calculate a new percentage column and check the result
ca_race_pct <- ca_race %>%
  mutate(pct = 100 * (estimate / summary_est))

ca_race_pct

# Group the dataset and filter the estimate
ca_largest <- ca_race %>%
  group_by(GEOID) %>%
  filter(estimate == max(estimate)) 

head(ca_largest)

# Group the dataset and get a breakdown of the results
ca_largest %>% 
  group_by(variable) %>% 
  tally()

# Use a tidy workflow to wrangle ACS data
wa_grouped <- wa_income %>%
  filter(variable != "B19001_001") %>%
  mutate(incgroup = case_when(
    variable < "B19001_008" ~ "below35k", 
    variable < "B19001_013" ~ "35kto75k", 
    TRUE ~ "above75k"
  )) %>%
  group_by(NAME, incgroup) %>%
  summarize(group_est = sum(estimate))

wa_grouped

# Map through ACS1 estimates to see how they change through the years
mi_cities <- map_df(2012:2016, function(x) {
  get_acs(geography = "place", 
          variables = c(totalpop = "B01003_001"), 
          state = "MI", 
          survey = "acs1", 
          year = x) %>%
    mutate(year = x)
})

Getting data from the 2012 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using FIPS code '26' for state 'MI'
Getting data from the 2013 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using FIPS code '26' for state 'MI'
Getting data from the 2014 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using FIPS code '26' for state 'MI'
Getting data from the 2015 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using FIPS code '26' for state 'MI'
Getting data from the 2016 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using FIPS code '26' for state 'MI'

mi_cities %>% arrange(NAME, year)

# Get data on elderly poverty by Census tract in Vermont
vt_eldpov <- get_acs(geography = "tract", 
                     variables = c(eldpovm = "B17001_016", 
                                   eldpovf = "B17001_030"), 
                     state = "VT")

Getting data from the 2012-2016 5-year ACS
Using FIPS code '50' for state 'VT'

Error in curl::curl_fetch_memory(url, handle = handle) : 
  Operation was aborted by an application callback

# Group the dataset and calculate a derived margin of error
vt_eldpov2 <- vt_eldpov %>%
  group_by(GEOID) %>%
  summarize(
    estmf = sum(estimate), 
    moemf = moe_sum(moe= moe, estimate = estimate)
  )

# Filter rows where newly-derived margin of error exceeds newly-derived estimate
moe_check2 <- filter(vt_eldpov2, moemf > estmf)

# Check proportion of rows where margin of error exceeds estimate
nrow(moe_check2) / nrow(vt_eldpov2)

[1] 0.5815217

# Request median household income data
maine_inc <- get_acs(geography = "county", 
                     variables = c(hhincome = "B19013_001"), 
                     state = "ME") 

Getting data from the 2012-2016 5-year ACS
Using FIPS code '23' for state 'ME'

# Generate horizontal error bars with dots
ggplot(maine_inc, aes(x = estimate, y = NAME)) + 
  geom_errorbarh(aes(xmin = estimate - moe, xmax = estimate + moe)) + 
  geom_point()

# Remove unnecessary content from the county's name
maine_inc2 <- maine_inc %>%
  mutate(NAME = str_replace(NAME, " County, Maine", ""))

# Build a margin of error plot incorporating your modifications
ggplot(maine_inc2, aes(x = estimate, y = reorder(NAME, estimate))) + 
  geom_errorbarh(aes(xmin = estimate - moe, xmax = estimate + moe)) + 
  geom_point(size = 3, color = "darkgreen") + 
  theme_grey(base_size = 14) + 
  labs(title = "Median household income", 
       subtitle = "Counties in Maine", 
       x = "ACS estimate (bars represent margins of error)", 
       y = "") + 
  scale_x_continuous(labels = scales::dollar)

Understanding Census geography and tigris basics

TIGER - Topologically integrated geologically encodind databse

tigris downloads line and shapes from Census Beuro and pulls them into R as Spatial objects.

Include geographic and tract regions. Also includes features such as roads and water features.

library(tigris)

To enable 
caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.

Attaching package: ‘tigris’

The following object is masked from ‘package:graphics’:

    plot

# Get a counties dataset for Colorado and plot it
co_counties <- counties(state = "CO")

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Using FIPS code '08' for state 'CO'

head(co_counties)

An object of class "SpatialPolygonsDataFrame"
Slot "data":

Slot "polygons":
[[1]]
An object of class "Polygons"
Slot "Polygons":
[[1]]
An object of class "Polygon"
Slot "labpt":
[1] -106.28156   38.08055

Slot "area":
[1] 0.8431487

Slot "hole":
[1] FALSE

Slot "ringDir":
[1] 1

Slot "coords":
             [,1]     [,2]
   [1,] -106.8714 37.94189
   [2,] -106.8720 37.94205
   [3,] -106.8738 37.94253
   [4,] -106.8744 37.94268
   [5,] -106.8746 37.94331
   [6,] -106.8753 37.94519
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   [8,] -106.8771 37.94858
   [9,] -106.8787 37.94916
  [10,] -106.8799 37.94986
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  [23,] -106.8978 37.96789
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  [25,] -106.9022 37.96564
  [26,] -106.9052 37.96390
  [27,] -106.9087 37.96405
  [28,] -106.9102 37.96527
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  [30,] -106.9116 37.96648
  [31,] -106.9136 37.96629
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  [59,] -106.9622 37.97032
  [60,] -106.9625 37.97232
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  [71,] -106.9869 37.96944
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  [73,] -106.9874 37.96834
  [74,] -106.9875 37.96795
  [75,] -106.9876 37.96639
  [76,] -106.9876 37.96374
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  [78,] -106.9898 37.96103
  [79,] -106.9925 37.96072
  [80,] -106.9944 37.96077
  [81,] -106.9960 37.96017
  [82,] -106.9969 37.96039
  [83,] -106.9977 37.95732
  [84,] -106.9993 37.95702
  [85,] -107.0006 37.95604
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  [87,] -107.0008 37.96400
  [88,] -107.0008 37.96561
  [89,] -107.0008 37.96599
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  [91,] -107.0010 37.97662
  [92,] -107.0012 37.98561
  [93,] -107.0015 38.00000
  [94,] -107.0015 38.00406
  [95,] -107.0015 38.00655
  [96,] -107.0016 38.01411
  [97,] -107.0016 38.01963
  [98,] -107.0016 38.02342
  [99,] -107.0016 38.02838
 [100,] -107.0016 38.03361
 [101,] -107.0017 38.03683
 [102,] -107.0017 38.04290
 [103,] -107.0017 38.04449
 [104,] -107.0017 38.04968
 [105,] -107.0017 38.05201
 [106,] -107.0017 38.05504
 [107,] -107.0018 38.06412
 [108,] -107.0018 38.06703
 [109,] -107.0018 38.07578
 [110,] -107.0018 38.07869
 [111,] -107.0018 38.08153
 [112,] -107.0019 38.08493
 [113,] -107.0019 38.09006
 [114,] -107.0019 38.09276
 [115,] -107.0019 38.09291
 [116,] -107.0019 38.09371
 [117,] -107.0018 38.09800
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 [119,] -107.0017 38.10280
 [120,] -107.0015 38.11485
 [121,] -107.0014 38.11858
 [122,] -107.0013 38.12386
 [123,] -107.0013 38.12499
 [124,] -107.0013 38.12750
 [125,] -107.0012 38.13501
 [126,] -107.0012 38.13751
 [127,] -107.0012 38.13864
 [128,] -107.0012 38.14204
 [129,] -107.0012 38.14318
 [130,] -107.0012 38.14340
 [131,] -107.0011 38.14395
 [132,] -107.0011 38.14629
 [133,] -107.0011 38.14707
 [134,] -107.0011 38.14825
 [135,] -107.0011 38.15180
 [136,] -107.0011 38.15255
 [137,] -107.0011 38.15299
 [138,] -107.0011 38.15313
 [139,] -107.0011 38.15332
 [140,] -107.0011 38.15395
 [141,] -107.0011 38.15432
 [142,] -107.0011 38.15466
 [143,] -107.0011 38.15998
 [144,] -107.0011 38.16488
 [145,] -107.0011 38.17017
 [146,] -107.0011 38.18250
 [147,] -107.0011 38.19122
 [148,] -107.0011 38.19555
 [149,] -107.0011 38.19993
 [150,] -107.0011 38.20285
 [151,] -107.0011 38.20578
 [152,] -107.0011 38.21141
 [153,] -107.0010 38.22832
 [154,] -107.0010 38.23396
 [155,] -107.0009 38.24076
 [156,] -107.0008 38.24757
 [157,] -107.0008 38.24878
 [158,] -107.0008 38.24999
 [159,] -107.0007 38.27396
 [160,] -107.0007 38.27977
 [161,] -107.0007 38.28715
 [162,] -107.0007 38.29453
 [163,] -107.0006 38.31327
 [164,] -107.0006 38.32377
 [165,] -107.0006 38.33201
 [166,] -107.0006 38.33436
 [167,] -107.0006 38.34140
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 [ reached getOption("max.print") -- omitted 1396 rows ]



Slot "plotOrder":
[1] 1

Slot "labpt":
[1] -107.67615   37.76404

Slot "ID":
[1] "227"

Slot "area":
[1] 0.1028336



Slot "plotOrder":
[1] 1 3 5 4 2 6

Slot "bbox":
         min       max
x -108.38107 -102.0448
y   36.99961   41.0026

Slot "proj4string":
CRS arguments:
 +proj=longlat +ellps=GRS80
+towgs84=0,0,0,0,0,0,0 +no_defs 

# Get a Census tracts dataset for Denver County, Colorado and plot it
denver_tracts <- tracts(state = "CO", county = "Denver")

Using FIPS code '08' for state 'CO'

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Using FIPS code '031' for 'Denver County'

plot(denver_tracts)

king_tracts <- tracts(state="WA", county = "King")

Using FIPS code '53' for state 'WA'

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Using FIPS code '033' for 'King County'

plot(king_tracts)

# Plot area water features for Lane County, Oregon
lane_water <- area_water(state = "OR", county = "Lane")

Using FIPS code '41' for state 'OR'
Using FIPS code '039' for 'Lane County'

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plot(lane_water)

king_water <- area_water(state = "WA", county = "King")

Using FIPS code '53' for state 'WA'
Using FIPS code '033' for 'King County'

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plot(king_water)

Looking at structure of the Spatial (sp) objects

# Check the class of the data
class(co_counties)

[1] "SpatialPolygonsDataFrame"
attr(,"package")
[1] "sp"

# Take a look at the information in the data slot
head(co_counties@data)

# Check the coordinate system of the data
co_counties@proj4string

CRS arguments:
 +proj=longlat +ellps=GRS80
+towgs84=0,0,0,0,0,0,0 +no_defs 

Difference between cartogragphic and tigris boundaries

# Get a counties dataset for Michigan
mi_tiger <- counties("MI")

Using FIPS code '26' for state 'MI'

# Get the equivalent cartographic boundary shapefile
mi_cb <- counties("MI", cb = TRUE)

Using FIPS code '26' for state 'MI'

# Overlay the two on a plot to make a comparison
plot(mi_tiger$geometry)
plot(mi_cb$geometry, add = TRUE, border = "red")

# Get a historic Census tract shapefile from 1990 for Williamson County, Texas
williamson90 <- tracts(state = "TX", county = "Williamson", 
                       cb = TRUE, year = 1990)

Using FIPS code '48' for state 'TX'
Using FIPS code '491' for 'Williamson County'

# Compare with a current dataset for 2016
williamson16 <- tracts(state = "TX", county = "Williamson", 
                       cb = TRUE, year = "2016")

Using FIPS code '48' for state 'TX'
Using FIPS code '491' for 'Williamson County'

# Plot the geometry to compare the results                       
par(mfrow = c(1, 2))
plot(williamson90$geometry)
plot(williamson16$geometry)

# Get Census tract boundaries for Oregon and Washington
or_tracts <- tracts("OR", cb = TRUE)

Using FIPS code '41' for state 'OR'

wa_tracts <- tracts("WA", cb = TRUE)

Using FIPS code '53' for state 'WA'

# Check the tigris attributes of each object
attr(or_tracts, "tigris")

[1] "tract"

attr(wa_tracts, "tigris")

[1] "tract"

# Combine the datasets then plot the result
or_wa_tracts <- rbind_tigris(or_tracts, wa_tracts)
plot(or_wa_tracts$geometry)

Tidy patterns can also be applied to combine multiple tigris (sf) objects together.

# Generate a vector of state codes and assign to new_england
new_england <- c("ME", "NH", "VT", "MA")

# Iterate through the states and request tract data for state
ne_tracts <- map(new_england, function(x) {
  tracts(state = x, cb = TRUE)
}) %>%
  rbind_tigris()

Using FIPS code '23' for state 'ME'

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Using FIPS code '33' for state 'NH'

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Using FIPS code '50' for state 'VT'

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Using FIPS code '25' for state 'MA'

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plot(ne_tracts$geometry)

tracts(state = "WA", house = "upper")

Using FIPS code '53' for state 'WA'
Error in load_tiger(url, tigris_type = "tract", ...) : 
  unused argument (house = "upper")

# Get boundaries for Texas and set the house parameter
tx_house <- state_legislative_districts(state = "TX", house = "lower", cb = TRUE)

Using FIPS code '48' for state 'TX'

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# Merge data on legislators to their corresponding boundaries
tx_joined <- left_join(tx_house, tx_members, by = c("NAME" = "District"))

Error in check_join(x, y) : object 'tx_members' not found

# Plot the legislative district boundaries
ggplot(tx_joined) + 
  geom_sf()

# Set fill aesthetic to map areas represented by Republicans and Democrats
ggplot(tx_joined, aes(fill = Party)) + 
  geom_sf()

# Set values so that Republican areas are red and Democratic areas are blue
ggplot(tx_joined, aes(fill = Party)) + 
  geom_sf() + 
  scale_fill_manual(values = c("R" = "red", "D" = "blue"))

# Draw a ggplot without gridlines and with an informative title
ggplot(tx_joined, aes(fill = Party)) + 
  geom_sf() + 
  coord_sf(crs = 3083, datum = NA) + 
  scale_fill_manual(values = c("R" = "red", "D" = "blue")) + 
  theme_minimal(base_size = 16) + 
  labs(title = "State House Districts in Texas")

library(tidycensus)
library(tidyverse)
library(sf)

# Get dataset with geometry set to TRUE
orange_value <- get_acs(geography = "tract", state = "CA", 
                    county = "Orange", 
                    variables = "B25077_001", 
                    geometry = TRUE)

Getting data from the 2012-2016 5-year ACS
Using FIPS code '06' for state 'CA'
Using FIPS code '059' for 'Orange County'

# Plot the estimate to view a map of the data
plot(orange_value["estimate"])

# Get an income dataset for Idaho by school district
idaho_income <- get_acs(geography = "school district (unified)", 
                        variables = "B19013_001", 
                        state = "ID")

# Get a school district dataset for Idaho
idaho_school <- school_districts(state = "ID", type = "unified", class = "sf")

# Join the income dataset to the boundaries dataset
id_school_joined <- left_join(idaho_school, idaho_income, idaho_school, by = "GEOID")

plot(id_school_joined["estimate"])

# Get a dataset of median home values from the 1-year ACS
state_value <- get_acs(geography = "state", 
                       variables = "B25077_001", 
                       survey = "acs1", 
                       geometry = TRUE, 
                       shift_geo = TRUE)

Getting data from the 2016 1-year ACS
The one-year ACS provides data for geographies with populations of 65,000 and greater.
Using feature geometry obtained from the albersusa package
Please note: Alaska and Hawaii are being shifted and are not to scale.

# Plot the dataset to view the shifted geometry
plot(state_value["estimate"])

# Set the color guide to FALSE and add a subtitle and caption to your map
ggplot(marin_value, aes(fill = estimate, color = estimate)) + 
  geom_sf() + 
  scale_fill_viridis_c(labels = scales::dollar) +  
  scale_color_viridis_c(guide = FALSE) + 
  theme_minimal() + 
  coord_sf(crs = 26911, datum = NA) + 
  labs(title = "Median owner-occupied housing value by Census tract", 
       subtitle = "Marin County, California", 
       caption = "Data source: 2012-2016 ACS.\nData acquired with the R tidycensus package.", 
       fill = "ACS estimate")

# Generate point centers
centers <- st_centroid(state_value)

# Set size parameter and the size range
ggplot() + 
  geom_sf(data = state_value, fill = "white") + 
  geom_sf(data = centers, aes(size = estimate), shape = 21, 
          fill = "lightblue", alpha = 0.7, show.legend = "point") + 
  scale_size_continuous(range = c(1, 20))

Other pacakges - censusapi - full census API access, very extensive - ipumsr - historical data - cancensus - Canadian census

Other Recommendations - Working with data in the tidyverse - data visualizeation with ggplot2 - interactive maps with leaflet in R